Overview

Dataset statistics

Number of variables13
Number of observations9994
Missing cells0
Missing cells (%)0.0%
Duplicate rows2448
Duplicate rows (%)24.5%
Total size in memory674.9 KiB
Average record size in memory69.1 B

Variable types

Numeric8
Categorical5

Alerts

Dataset has 2448 (24.5%) duplicate rowsDuplicates
Order Date is highly overall correlated with Ship DateHigh correlation
Ship Date is highly overall correlated with Order DateHigh correlation
Sales is highly overall correlated with ProfitHigh correlation
Discount is highly overall correlated with ProfitHigh correlation
Profit is highly overall correlated with Sales and 1 other fieldsHigh correlation
Product ID_id is highly overall correlated with Category and 1 other fieldsHigh correlation
Category is highly overall correlated with Product ID_id and 1 other fieldsHigh correlation
Sub-Category is highly overall correlated with Product ID_id and 1 other fieldsHigh correlation
Discount has 4798 (48.0%) zerosZeros

Reproduction

Analysis started2023-04-09 04:39:24.852418
Analysis finished2023-04-09 04:39:34.175616
Duration9.32 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Order Date
Real number (ℝ)

Distinct1237
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14619.748
Minimum13887.072
Maximum15145.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-04-09T13:39:34.300484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13887.072
5-th percentile14006.866
Q114323.392
median14668.992
Q314947.2
95-th percentile15117.408
Maximum15145.92
Range1258.848
Interquartile range (IQR)623.808

Descriptive statistics

Standard deviation363.30282
Coefficient of variation (CV)0.024850142
Kurtosis-1.1532496
Mean14619.748
Median Absolute Deviation (MAD)304.992
Skewness-0.26783343
Sum1.4610976 × 108
Variance131988.94
MonotonicityNot monotonic
2023-04-09T13:39:34.441294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14730.336 38
 
0.4%
15043.104 36
 
0.4%
14787.36 35
 
0.4%
15120.864 34
 
0.3%
15121.728 34
 
0.3%
15127.776 33
 
0.3%
15104.448 30
 
0.3%
15126.912 30
 
0.3%
15049.152 29
 
0.3%
14814.144 28
 
0.3%
Other values (1227) 9667
96.7%
ValueCountFrequency (%)
13887.072 1
 
< 0.1%
13887.936 3
 
< 0.1%
13888.8 1
 
< 0.1%
13889.664 9
0.1%
13890.528 2
 
< 0.1%
13892.256 2
 
< 0.1%
13893.12 2
 
< 0.1%
13893.984 1
 
< 0.1%
13895.712 11
0.1%
13896.576 1
 
< 0.1%
ValueCountFrequency (%)
15145.92 7
 
0.1%
15145.056 12
0.1%
15144.192 19
0.2%
15143.328 2
 
< 0.1%
15142.464 4
 
< 0.1%
15141.6 23
0.2%
15140.736 16
0.2%
15139.872 15
0.2%
15139.008 27
0.3%
15138.144 11
0.1%

Ship Date
Real number (ℝ)

Distinct1334
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14623.168
Minimum13890.528
Maximum15151.104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-04-09T13:39:34.573789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13890.528
5-th percentile14011.186
Q114326.848
median14671.584
Q314950.656
95-th percentile15120.864
Maximum15151.104
Range1260.576
Interquartile range (IQR)623.808

Descriptive statistics

Standard deviation363.2733
Coefficient of variation (CV)0.024842312
Kurtosis-1.1532138
Mean14623.168
Median Absolute Deviation (MAD)305.856
Skewness-0.2678001
Sum1.4614394 × 108
Variance131967.49
MonotonicityNot monotonic
2023-04-09T13:39:34.710242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14502.24 35
 
0.4%
15063.84 34
 
0.3%
15125.184 32
 
0.3%
15112.224 32
 
0.3%
15046.56 30
 
0.3%
15130.368 30
 
0.3%
15054.336 30
 
0.3%
14105.664 27
 
0.3%
15048.288 27
 
0.3%
15109.632 26
 
0.3%
Other values (1324) 9691
97.0%
ValueCountFrequency (%)
13890.528 2
 
< 0.1%
13891.392 4
< 0.1%
13893.12 7
0.1%
13894.848 3
 
< 0.1%
13895.712 2
 
< 0.1%
13896.576 1
 
< 0.1%
13897.44 8
0.1%
13898.304 1
 
< 0.1%
13899.168 1
 
< 0.1%
13900.032 9
0.1%
ValueCountFrequency (%)
15151.104 2
 
< 0.1%
15150.24 5
 
0.1%
15149.376 7
 
0.1%
15148.512 8
 
0.1%
15147.648 20
0.2%
15146.784 6
 
0.1%
15145.92 8
 
0.1%
15145.056 12
0.1%
15144.192 23
0.2%
15143.328 12
0.1%

Ship Mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Standard Class
5968 
Second Class
1945 
First Class
1538 
Same Day
 
543

Length

Max length14
Median length14
Mean length12.823094
Min length8

Characters and Unicode

Total characters128154
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond Class
2nd rowSecond Class
3rd rowSecond Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 5968
59.7%
Second Class 1945
 
19.5%
First Class 1538
 
15.4%
Same Day 543
 
5.4%

Length

2023-04-09T13:39:34.835068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:39:34.946957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
class 9451
47.3%
standard 5968
29.9%
second 1945
 
9.7%
first 1538
 
7.7%
same 543
 
2.7%
day 543
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
l 9451
7.4%
C 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
r 7506
 
5.9%
t 7506
 
5.9%
Other values (8) 11083
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98172
76.6%
Uppercase Letter 19988
 
15.6%
Space Separator 9994
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22473
22.9%
s 20440
20.8%
d 13881
14.1%
l 9451
9.6%
n 7913
 
8.1%
r 7506
 
7.6%
t 7506
 
7.6%
e 2488
 
2.5%
c 1945
 
2.0%
o 1945
 
2.0%
Other values (3) 2624
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 9451
47.3%
S 8456
42.3%
F 1538
 
7.7%
D 543
 
2.7%
Space Separator
ValueCountFrequency (%)
9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118160
92.2%
Common 9994
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22473
19.0%
s 20440
17.3%
d 13881
11.7%
l 9451
8.0%
C 9451
8.0%
S 8456
 
7.2%
n 7913
 
6.7%
r 7506
 
6.4%
t 7506
 
6.4%
e 2488
 
2.1%
Other values (7) 8595
 
7.3%
Common
ValueCountFrequency (%)
9994
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
l 9451
7.4%
C 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
r 7506
 
5.9%
t 7506
 
5.9%
Other values (8) 11083
8.6%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
Consumer
5191 
Corporate
3020 
Home Office
1783 

Length

Max length11
Median length8
Mean length8.8374024
Min length8

Characters and Unicode

Total characters88321
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowCorporate
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 5191
51.9%
Corporate 3020
30.2%
Home Office 1783
 
17.8%

Length

2023-04-09T13:39:35.044131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:39:35.157151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
consumer 5191
44.1%
corporate 3020
25.6%
home 1783
 
15.1%
office 1783
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
n 5191
 
5.9%
s 5191
 
5.9%
u 5191
 
5.9%
f 3566
 
4.0%
t 3020
 
3.4%
Other values (7) 14955
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74761
84.6%
Uppercase Letter 11777
 
13.3%
Space Separator 1783
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 13014
17.4%
e 11777
15.8%
r 11231
15.0%
m 6974
9.3%
n 5191
 
6.9%
s 5191
 
6.9%
u 5191
 
6.9%
f 3566
 
4.8%
t 3020
 
4.0%
p 3020
 
4.0%
Other values (3) 6586
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 8211
69.7%
H 1783
 
15.1%
O 1783
 
15.1%
Space Separator
ValueCountFrequency (%)
1783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86538
98.0%
Common 1783
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 13014
15.0%
e 11777
13.6%
r 11231
13.0%
C 8211
9.5%
m 6974
8.1%
n 5191
 
6.0%
s 5191
 
6.0%
u 5191
 
6.0%
f 3566
 
4.1%
t 3020
 
3.5%
Other values (6) 13172
15.2%
Common
ValueCountFrequency (%)
1783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
n 5191
 
5.9%
s 5191
 
5.9%
u 5191
 
5.9%
f 3566
 
4.0%
t 3020
 
3.4%
Other values (7) 14955
16.9%

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
West
3203 
East
2848 
Central
2323 
South
1620 

Length

Max length7
Median length4
Mean length4.8594156
Min length4

Characters and Unicode

Total characters48565
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowWest
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
West 3203
32.0%
East 2848
28.5%
Central 2323
23.2%
South 1620
16.2%

Length

2023-04-09T13:39:35.257929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:39:35.376750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
west 3203
32.0%
east 2848
28.5%
central 2323
23.2%
south 1620
16.2%

Most occurring characters

ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38571
79.4%
Uppercase Letter 9994
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9994
25.9%
s 6051
15.7%
e 5526
14.3%
a 5171
13.4%
n 2323
 
6.0%
r 2323
 
6.0%
l 2323
 
6.0%
o 1620
 
4.2%
u 1620
 
4.2%
h 1620
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
W 3203
32.0%
E 2848
28.5%
C 2323
23.2%
S 1620
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 48565
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
Office Supplies
6026 
Furniture
2121 
Technology
1847 

Length

Max length15
Median length15
Mean length12.802582
Min length9

Characters and Unicode

Total characters127949
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture
2nd rowFurniture
3rd rowOffice Supplies
4th rowFurniture
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 6026
60.3%
Furniture 2121
 
21.2%
Technology 1847
 
18.5%

Length

2023-04-09T13:39:35.477350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:39:35.590130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
office 6026
37.6%
supplies 6026
37.6%
furniture 2121
 
13.2%
technology 1847
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
s 6026
 
4.7%
S 6026
 
4.7%
Other values (10) 29560
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 105903
82.8%
Uppercase Letter 16020
 
12.5%
Space Separator 6026
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16020
15.1%
i 14173
13.4%
p 12052
11.4%
f 12052
11.4%
u 10268
9.7%
c 7873
7.4%
l 7873
7.4%
s 6026
 
5.7%
r 4242
 
4.0%
n 3968
 
3.7%
Other values (5) 11356
10.7%
Uppercase Letter
ValueCountFrequency (%)
O 6026
37.6%
S 6026
37.6%
F 2121
 
13.2%
T 1847
 
11.5%
Space Separator
ValueCountFrequency (%)
6026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121923
95.3%
Common 6026
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16020
13.1%
i 14173
11.6%
p 12052
9.9%
f 12052
9.9%
u 10268
8.4%
c 7873
 
6.5%
l 7873
 
6.5%
O 6026
 
4.9%
s 6026
 
4.9%
S 6026
 
4.9%
Other values (9) 23534
19.3%
Common
ValueCountFrequency (%)
6026
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
s 6026
 
4.7%
S 6026
 
4.7%
Other values (10) 29560
23.1%

Sub-Category
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
Binders
1523 
Paper
1370 
Furnishings
957 
Phones
889 
Storage
846 
Other values (12)
4409 

Length

Max length11
Median length9
Mean length7.191715
Min length3

Characters and Unicode

Total characters71874
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBookcases
2nd rowChairs
3rd rowLabels
4th rowTables
5th rowStorage

Common Values

ValueCountFrequency (%)
Binders 1523
15.2%
Paper 1370
13.7%
Furnishings 957
9.6%
Phones 889
8.9%
Storage 846
8.5%
Art 796
8.0%
Accessories 775
7.8%
Chairs 617
6.2%
Appliances 466
 
4.7%
Labels 364
 
3.6%
Other values (7) 1391
13.9%

Length

2023-04-09T13:39:35.700608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 1523
15.2%
paper 1370
13.7%
furnishings 957
9.6%
phones 889
8.9%
storage 846
8.5%
art 796
8.0%
accessories 775
7.8%
chairs 617
6.2%
appliances 466
 
4.7%
labels 364
 
3.6%
Other values (7) 1391
13.9%

Most occurring characters

ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61880
86.1%
Uppercase Letter 9994
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9934
16.1%
e 8870
14.3%
r 7169
11.6%
i 5668
9.2%
n 5378
8.7%
a 4542
7.3%
o 3288
 
5.3%
p 3004
 
4.9%
h 2578
 
4.2%
c 2359
 
3.8%
Other values (8) 9090
14.7%
Uppercase Letter
ValueCountFrequency (%)
P 2259
22.6%
A 2037
20.4%
B 1751
17.5%
F 1174
11.7%
S 1036
10.4%
C 685
 
6.9%
L 364
 
3.6%
T 319
 
3.2%
E 254
 
2.5%
M 115
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 71874
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Sales
Real number (ℝ)

Distinct5825
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.858
Minimum0.444
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-04-09T13:39:35.831538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile4.98
Q117.28
median54.49
Q3209.94
95-th percentile956.98425
Maximum22638.48
Range22638.036
Interquartile range (IQR)192.66

Descriptive statistics

Standard deviation623.2451
Coefficient of variation (CV)2.7114353
Kurtosis305.31175
Mean229.858
Median Absolute Deviation (MAD)45.406
Skewness12.972752
Sum2297200.9
Variance388434.46
MonotonicityNot monotonic
2023-04-09T13:39:35.958102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 56
 
0.6%
19.44 39
 
0.4%
15.552 39
 
0.4%
25.92 36
 
0.4%
10.368 36
 
0.4%
32.4 28
 
0.3%
17.94 21
 
0.2%
6.48 21
 
0.2%
20.736 19
 
0.2%
14.94 17
 
0.2%
Other values (5815) 9682
96.9%
ValueCountFrequency (%)
0.444 1
 
< 0.1%
0.556 1
 
< 0.1%
0.836 1
 
< 0.1%
0.852 1
 
< 0.1%
0.876 1
 
< 0.1%
0.898 1
 
< 0.1%
0.984 1
 
< 0.1%
0.99 1
 
< 0.1%
1.044 1
 
< 0.1%
1.08 3
< 0.1%
ValueCountFrequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.976 1
< 0.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7895737
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-04-09T13:39:36.059310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2251097
Coefficient of variation (CV)0.58716622
Kurtosis1.9918894
Mean3.7895737
Median Absolute Deviation (MAD)1
Skewness1.2785448
Sum37873
Variance4.9511131
MonotonicityNot monotonic
2023-04-09T13:39:36.157659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2409
24.1%
2 2402
24.0%
5 1230
12.3%
4 1191
11.9%
1 899
 
9.0%
7 606
 
6.1%
6 572
 
5.7%
9 258
 
2.6%
8 257
 
2.6%
10 57
 
0.6%
Other values (4) 113
 
1.1%
ValueCountFrequency (%)
1 899
 
9.0%
2 2402
24.0%
3 2409
24.1%
4 1191
11.9%
5 1230
12.3%
6 572
 
5.7%
7 606
 
6.1%
8 257
 
2.6%
9 258
 
2.6%
10 57
 
0.6%
ValueCountFrequency (%)
14 29
 
0.3%
13 27
 
0.3%
12 23
 
0.2%
11 34
 
0.3%
10 57
 
0.6%
9 258
 
2.6%
8 257
 
2.6%
7 606
6.1%
6 572
5.7%
5 1230
12.3%

Discount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15620272
Minimum0
Maximum0.8
Zeros4798
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-04-09T13:39:36.263527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20645197
Coefficient of variation (CV)1.3216925
Kurtosis2.4095461
Mean0.15620272
Median Absolute Deviation (MAD)0.2
Skewness1.6842947
Sum1561.09
Variance0.042622415
MonotonicityNot monotonic
2023-04-09T13:39:36.365352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4798
48.0%
0.2 3657
36.6%
0.7 418
 
4.2%
0.8 300
 
3.0%
0.3 227
 
2.3%
0.4 206
 
2.1%
0.6 138
 
1.4%
0.1 94
 
0.9%
0.5 66
 
0.7%
0.15 52
 
0.5%
Other values (2) 38
 
0.4%
ValueCountFrequency (%)
0 4798
48.0%
0.1 94
 
0.9%
0.15 52
 
0.5%
0.2 3657
36.6%
0.3 227
 
2.3%
0.32 27
 
0.3%
0.4 206
 
2.1%
0.45 11
 
0.1%
0.5 66
 
0.7%
0.6 138
 
1.4%
ValueCountFrequency (%)
0.8 300
 
3.0%
0.7 418
 
4.2%
0.6 138
 
1.4%
0.5 66
 
0.7%
0.45 11
 
0.1%
0.4 206
 
2.1%
0.32 27
 
0.3%
0.3 227
 
2.3%
0.2 3657
36.6%
0.15 52
 
0.5%

Profit
Real number (ℝ)

Distinct7287
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.656896
Minimum-6599.978
Maximum8399.976
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size78.2 KiB
2023-04-09T13:39:36.495361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-53.03092
Q11.72875
median8.6665
Q329.364
95-th percentile168.4704
Maximum8399.976
Range14999.954
Interquartile range (IQR)27.63525

Descriptive statistics

Standard deviation234.26011
Coefficient of variation (CV)8.1746504
Kurtosis397.18851
Mean28.656896
Median Absolute Deviation (MAD)10.77855
Skewness7.5614316
Sum286397.02
Variance54877.798
MonotonicityNot monotonic
2023-04-09T13:39:36.618880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.7%
6.2208 43
 
0.4%
9.3312 38
 
0.4%
5.4432 32
 
0.3%
3.6288 32
 
0.3%
15.552 26
 
0.3%
12.4416 21
 
0.2%
7.2576 19
 
0.2%
3.1104 18
 
0.2%
9.072 11
 
0.1%
Other values (7277) 9689
96.9%
ValueCountFrequency (%)
-6599.978 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2639.9912 1
< 0.1%
-2287.782 1
< 0.1%
-1862.3124 1
< 0.1%
-1850.9464 1
< 0.1%
-1811.0784 1
< 0.1%
ValueCountFrequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2799.984 1
< 0.1%
2591.9568 1
< 0.1%
2504.2216 1
< 0.1%

Customer ID_id
Real number (ℝ)

Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400.46038
Minimum0
Maximum792
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-04-09T13:39:36.757304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q1205.25
median405.5
Q3602
95-th percentile753
Maximum792
Range792
Interquartile range (IQR)396.75

Descriptive statistics

Standard deviation228.58558
Coefficient of variation (CV)0.57080698
Kurtosis-1.1915803
Mean400.46038
Median Absolute Deviation (MAD)197.5
Skewness-0.025462504
Sum4002201
Variance52251.366
MonotonicityNot monotonic
2023-04-09T13:39:36.892643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
787 37
 
0.4%
387 34
 
0.3%
482 34
 
0.3%
606 34
 
0.3%
147 32
 
0.3%
720 32
 
0.3%
349 32
 
0.3%
257 32
 
0.3%
791 31
 
0.3%
275 31
 
0.3%
Other values (783) 9665
96.7%
ValueCountFrequency (%)
0 11
0.1%
1 15
0.2%
2 12
0.1%
3 18
0.2%
4 6
 
0.1%
5 18
0.2%
6 20
0.2%
7 12
0.1%
8 14
0.1%
9 14
0.1%
ValueCountFrequency (%)
792 9
 
0.1%
791 31
0.3%
790 12
 
0.1%
789 8
 
0.1%
788 28
0.3%
787 37
0.4%
786 18
0.2%
785 9
 
0.1%
784 8
 
0.1%
783 14
 
0.1%

Product ID_id
Real number (ℝ)

Distinct1862
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean898.47238
Minimum0
Maximum1861
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-04-09T13:39:37.037985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q1453
median863
Q31347
95-th percentile1758.35
Maximum1861
Range1861
Interquartile range (IQR)894

Descriptive statistics

Standard deviation526.70844
Coefficient of variation (CV)0.58622664
Kurtosis-1.1364991
Mean898.47238
Median Absolute Deviation (MAD)453
Skewness0.056258336
Sum8979333
Variance277421.79
MonotonicityNot monotonic
2023-04-09T13:39:37.166218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1144 19
 
0.2%
1569 18
 
0.2%
295 16
 
0.2%
65 15
 
0.2%
1566 15
 
0.2%
93 15
 
0.2%
1517 15
 
0.2%
831 14
 
0.1%
694 14
 
0.1%
97 14
 
0.1%
Other values (1852) 9839
98.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3
 
< 0.1%
2 5
0.1%
3 6
0.1%
4 2
 
< 0.1%
5 5
0.1%
6 10
0.1%
7 5
0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
1861 9
0.1%
1860 2
 
< 0.1%
1859 3
 
< 0.1%
1858 5
0.1%
1857 3
 
< 0.1%
1856 7
0.1%
1855 7
0.1%
1854 5
0.1%
1853 1
 
< 0.1%
1852 4
< 0.1%

Interactions

2023-04-09T13:39:32.570358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:25.516823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:26.957457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.879403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.748387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.662409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.620699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.550146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:32.697354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:25.630765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.074782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.988631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.860748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.781043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.736534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.668718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:32.817857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:25.745845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.184716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.095248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.971530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.900633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.851519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.789006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:32.924273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:25.848759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.289343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.190545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.072086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.008117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.955631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.909802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:33.039589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:25.964942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.400960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.298995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.180284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.127739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.069023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:32.030095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:33.391678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:26.086893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.523609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.415390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.302653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.251272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.194219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:32.169151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:33.520320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:26.208367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.639926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.525997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.418659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.373489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.310900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:32.302904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:33.639385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:26.330079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:27.761983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:28.638379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:29.540609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:30.502298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:31.433110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:39:32.436245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-09T13:39:37.291264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Order DateShip DateSalesQuantityDiscountProfitCustomer ID_idProduct ID_idShip ModeSegmentRegionCategorySub-Category
Order Date1.0001.000-0.002-0.000-0.0040.001-0.0380.0010.0390.0360.0600.0000.000
Ship Date1.0001.000-0.002-0.000-0.0040.001-0.0380.0010.0410.0390.0580.0000.000
Sales-0.002-0.0021.0000.327-0.0570.5180.0070.0600.0000.0020.0000.0720.142
Quantity-0.000-0.0000.3271.000-0.0010.2340.019-0.0100.0000.0120.0000.0000.000
Discount-0.004-0.004-0.057-0.0011.000-0.5430.017-0.1160.0270.0050.2940.3770.353
Profit0.0010.0010.5180.234-0.5431.000-0.0050.1770.0050.0000.0210.0560.130
Customer ID_id-0.038-0.0380.0070.0190.017-0.0051.0000.0050.0300.1340.0450.0000.000
Product ID_id0.0010.0010.060-0.010-0.1160.1770.0051.0000.0000.0090.0000.9750.841
Ship Mode0.0390.0410.0000.0000.0270.0050.0300.0001.0000.0330.0220.0000.007
Segment0.0360.0390.0020.0120.0050.0000.1340.0090.0331.0000.0000.0000.000
Region0.0600.0580.0000.0000.2940.0210.0450.0000.0220.0001.0000.0000.000
Category0.0000.0000.0720.0000.3770.0560.0000.9750.0000.0000.0001.0000.999
Sub-Category0.0000.0000.1420.0000.3530.1300.0000.8410.0070.0000.0000.9991.000

Missing values

2023-04-09T13:39:33.805455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T13:39:34.061876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Order DateShip DateShip ModeSegmentRegionCategorySub-CategorySalesQuantityDiscountProfitCustomer ID_idProduct ID_id
014785.63214788.224Second ClassConsumerSouthFurnitureBookcases261.960020.0041.913614312
114785.63214788.224Second ClassConsumerSouthFurnitureChairs731.940030.00219.582014355
214656.89614660.352Second ClassCorporateWestOffice SuppliesLabels14.620020.006.8714237946
314445.21614451.264Standard ClassConsumerSouthFurnitureTables957.577550.45-383.0310705319
414445.21614451.264Standard ClassConsumerSouthOffice SuppliesStorage22.368020.202.51647051316
514022.72014027.040Standard ClassConsumerWestFurnitureFurnishings48.860070.0014.169488185
614022.72014027.040Standard ClassConsumerWestOffice SuppliesArt7.280040.001.965688562
714022.72014027.040Standard ClassConsumerWestTechnologyPhones907.152060.2090.7152881761
814022.72014027.040Standard ClassConsumerWestOffice SuppliesBinders18.504030.205.782588794
914022.72014027.040Standard ClassConsumerWestOffice SuppliesAppliances114.900050.0034.470088437
Order DateShip DateShip ModeSegmentRegionCategorySub-CategorySalesQuantityDiscountProfitCustomer ID_idProduct ID_id
998414318.20814323.392Standard ClassConsumerEastOffice SuppliesLabels31.500100.015.1200238995
998514318.20814323.392Standard ClassConsumerEastOffice SuppliesSupplies55.60040.016.12402381425
998614751.07214754.528Standard ClassConsumerWestTechnologyAccessories36.24010.015.22085201558
998715108.76815112.224Standard ClassCorporateSouthTechnologyAccessories79.99010.028.79646171498
998815108.76815112.224Standard ClassCorporateSouthTechnologyPhones206.10050.055.64706171826
998913902.62413904.352Second ClassConsumerSouthFurnitureFurnishings25.24830.24.1028737200
999014880.67214884.992Standard ClassConsumerWestFurnitureFurnishings91.96020.015.6332190164
999114880.67214884.992Standard ClassConsumerWestTechnologyPhones258.57620.219.39321901816
999214880.67214884.992Standard ClassConsumerWestOffice SuppliesPaper29.60040.013.32001901247
999314938.56014942.880Second ClassConsumerWestOffice SuppliesAppliances243.16020.072.9480130433

Duplicate rows

Most frequently occurring

Order DateShip DateShip ModeSegmentRegionCategorySub-CategorySalesQuantityDiscountProfitCustomer ID_idProduct ID_id# duplicates
230013982.11213985.568Standard ClassHome OfficeEastFurnitureChairs281.37220.3-12.0588449992
013982.11213985.568First ClassConsumerCentralFurnitureAccessories281.37220.3-12.0588449990
113982.11213985.568First ClassConsumerCentralFurnitureAppliances281.37220.3-12.0588449990
213982.11213985.568First ClassConsumerCentralFurnitureArt281.37220.3-12.0588449990
313982.11213985.568First ClassConsumerCentralFurnitureBinders281.37220.3-12.0588449990
413982.11213985.568First ClassConsumerCentralFurnitureBookcases281.37220.3-12.0588449990
513982.11213985.568First ClassConsumerCentralFurnitureChairs281.37220.3-12.0588449990
613982.11213985.568First ClassConsumerCentralFurnitureCopiers281.37220.3-12.0588449990
713982.11213985.568First ClassConsumerCentralFurnitureEnvelopes281.37220.3-12.0588449990
813982.11213985.568First ClassConsumerCentralFurnitureFasteners281.37220.3-12.0588449990